Competition over resources optimized fuzzy TIDF controller for frequency stabilization of hybrid micro‐grid system

Patel, Sunita; Mohanty, Banaja; Hasanien, Hany M.

doi:10.1002/2050-7038.12513

Cited by 19 publications

(24 citation statements)

References 34 publications

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“…The SOC is an important constraint condition of the energy storage system. The SOC constraint optimization of the hybrid energy storage system can improve the accuracy of power allocation [23].

SOC false(t false) = \{\begin{matrix} left normalSOC (t - 1) + \frac{P_{normalsc 1} normalΔ t η_{c}}{Q} P_{normalsc 1} (t) > 0 \\ left normalSOC (t - 1) - \frac{P_{normalsc 1} normalΔ t}{Q η_{d}} P_{normalsc 1} (t) < 0 \end{matrix} .

…”

Section: The Proposed Methods Based On Adaptive Power Decomposition Opmentioning

confidence: 99%

Power fluctuation and allocation of hybrid energy storage system based on optimal exponential smoothing method and energy entropy

Zheng

Xiu-bo

2020

IET Generation Trans & Dist

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In order to solve the problems of power quality reduction and power fluctuation caused by large-scale wind power grid-connected, an advanced control strategy to smooth the power fluctuation and allocation of hybrid energy storage system is proposed. Based on theoretical researches, the mathematical model of hybrid energy storage system is adopted to well analyse the fluctuation and smoothing strategy of wind power. Compared with the traditional filtering algorithms, the study proposes a method combined optimal exponential smoothing with complete ensemble empirical mode decomposition with adaptive noise and normalized energy entropy to improve the accuracy of grid-connected output power and power allocation. Furthermore, the fuzzy control theory is used to improve the reliability of the algorithm after obtaining the smoothed power output and initial power allocation instructions. To prove the validity of the algorithm, case studies are constructed to demonstrate the performance in this paper. Experiments and example simulations show that the proposed method can effectively realize adaptive power allocation and improve the accuracy of identification. After effectively improving the efficiency and service life of the energy storage system, it provides a basis for large-scale grid operation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The SOC is an important constraint condition of the energy storage system. The SOC constraint optimization of the hybrid energy storage system can improve the accuracy of power allocation [23].

SOC false(t false) = \{\begin{matrix} left normalSOC (t - 1) + \frac{P_{normalsc 1} normalΔ t η_{c}}{Q} P_{normalsc 1} (t) > 0 \\ left normalSOC (t - 1) - \frac{P_{normalsc 1} normalΔ t}{Q η_{d}} P_{normalsc 1} (t) < 0 \end{matrix} .

…”

Section: The Proposed Methods Based On Adaptive Power Decomposition Opmentioning

confidence: 99%

Power fluctuation and allocation of hybrid energy storage system based on optimal exponential smoothing method and energy entropy

Zheng

Xiu-bo

2020

IET Generation Trans & Dist

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“…Moreover, the storage in each area is considered to have the same capacity as the renewable energy connected to it. All components of the microgrid system are assumed as linear models using the first-order transfer functions as illustrated in Table 1 [48]. For more details, models of different components of the hybrid microgrid system are briefly described in the following sub-sections:…”

Section: System Modelingmentioning

confidence: 99%

“…Different optimization algorithms were presented in the literature to adjust the fuzzy controller gains such as the grey wolf algorithm in [37], the multi-verse algorithm in [40], the harmony search algorithm in [43], the ant colony algorithm in [44], and the genetic algorithm in [45,46]. Whereas for frequency stabilization for hybrid systems, Fuzzy-PIDF controllers were proposed in [47] based on the PSO algorithm and in [48] based on the competition over resources (COR) algorithm. In [49], a Fuzzy-PID controller was proposed for the automatic generation control of a hydro-thermal power system based on the glow-worm swarm optimization technique.…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [52] proposed a fuzzy self-tuning PID controller for LFC based on the Tribe-DE optimization algorithm in parametric uncertainties in addition to the presence of external perturbations. Although the previous fuzzy-based LFC structures had demonstrated a good system response, they could not be considered fully optimized controllers because the input and output scaling factors were the only optimized parameters while the boundaries of the membership functions were selected by the designer experience [37][38][39][40][41][42][43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

“…The hybrid microgrid system under study includes PV and wind energy sources with real irradiance and wind speed data as well as energy storage devices. The results of the proposed controller are compared with different controllers such as MPA-PIDF and recently introduced CRO-PIDF, CRO-FPIDF [48], and PSO-PIDF controllers. Moreover, the robustness of the proposed MPA-FPIDF controller is tested under different load perturbations, system parameters variation and uncertainties of renewable energy sources such as wind speed fluctuation and solar irradiance variation.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

et al. 2021

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This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK. INDEX TERMSFuzzy control, load frequency control, marine predator algorithm, microgrid system, PIDF controller, renewable energy, storage systems. NOMENCLATURE A Rotor swept area ACE Area control error B1, B2 Frequency bias coefficients BES Battery energy storage CE Change of error Predator step size control ratio Power coefficient COR Competition over resources E Error FF Fitness function FLC Fuzzy logic controller FPIDF Fuzzy proportional-integral-derivative with filter G Solar radiation ( ) Controller transfer function Solar irradiance under standard test conditions 1 , 2 Input scaling gains 12

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Sine cosine adopted Harris' hawks optimization for function optimization and power system frequency controller design

Mohanty

Panda

2021

Int Trans Electr Energ Syst

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Summary A considerable number of intermittent renewable resources like photovoltaic generation and wind energy when integrated into the conventional grid technology causes serious issues in the power systems like frequency instability. So an intelligent controller is desired for steady and reliable operation of the electric grid. This paper deals with the frequency control of hybrid power system (HPS) employing a sine cosine adopted Harris' hawks optimization (SCaHHO) technique. The proposed technique aims at improving the performance of the original Harris' hawks optimization (HHO) algorithm by incorporating a sine and cosine function in the calculation of escaping energy and chances of escaping of prey respectively to emphasize the exploitation process of hawks. The effectiveness of the SCaHHO technique is validated with the novel HHO technique as well as moth flame optimization, sine cosine algorithm, grey wolf optimization, salp swarm algorithm and gravitational search algorithm using 23 standard test functions. A non‐parametric statistical investigation is also carried out to verify the effectiveness of the suggested technique. Later the SCaHHO technique is utilized to tune a newly proposed adaptive fuzzy proportional integral derivative controller (AFPID) for frequency control of HPS. The suggested SCaHHO‐tuned AFPID controller achieves an improved control action by moderating the frequency fluctuations as compared to the PID controller. Hardware‐in‐loop validation of the proposed load frequency control scheme is also carried out using OPAL‐RT to measure its fidelity with the numerical simulation results.

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Competition over resources optimized fuzzy TIDF controller for frequency stabilization of hybrid micro‐grid system

Cited by 19 publications

References 34 publications

Power fluctuation and allocation of hybrid energy storage system based on optimal exponential smoothing method and energy entropy

Power fluctuation and allocation of hybrid energy storage system based on optimal exponential smoothing method and energy entropy

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

Sine cosine adopted Harris' hawks optimization for function optimization and power system frequency controller design

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